CN111428627B - Mountain landform remote sensing extraction method and system - Google Patents
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Abstract
The invention belongs to the technical field of remote sensing, and discloses a mountain landform remote sensing extraction method and a mountain landform remote sensing extraction system. According to the method, the segmentation problem caused by uncertainty of gray level membership and uncertainty of segmentation decision is effectively solved through the image segmentation module, more accurate fitting of the distribution characteristics of the complex histogram of the high-resolution remote sensing data is realized, noise is well overcome, and the segmentation precision of the mountain landform remote sensing image is improved; meanwhile, the sensory data classification module utilizes a classification system to learn and self-organize the spatiotemporal data, a hierarchical classification system can be dynamically and continuously adjusted and perfected according to new data, and dynamic organization and classification management of mountain remote sensing data are realized.
Description
Technical Field
The invention belongs to the technical field of remote sensing, and particularly relates to a mountain landform remote sensing extraction method and system.
Background
Remote sensing (remote sensing) refers to a non-contact, remote sensing technique. The radiation and reflection characteristics of the electromagnetic wave of the object are detected by using a sensor/remote sensor. The method is a science and technology for acquiring information (such as electric field, magnetic field, electromagnetic wave, seismic wave and the like) of reflected, radiated or scattered electromagnetic waves, and extracting, judging, processing, analyzing and applying the information. Remote sensing, which can be understood as remote sensing in a literal sense, generally refers to all contactless remote detection; remote sensing is a new technology which combines inductive remote sensing and resource management monitoring (such as resource management of trees, grassland, soil, water, minerals, farm crops, fish, wildlife, etc.) on the earth surface through remote measuring instruments on platforms such as artificial earth satellites, aviation, etc. However, the existing mountain landform remote sensing image has poor segmentation precision; meanwhile, the traditional remote sensing data organization is simply put in storage, and the database is searched and information is acquired according to application requirements, so that the searching efficiency and the information acquisition capacity are limited on one hand, the information in the database cannot be visually checked on the other hand, and the utilization value of the data is greatly limited.
In summary, the problems of the prior art are as follows:
the existing mountain landform remote sensing image has poor segmentation precision; meanwhile, the traditional remote sensing data organization is simply put in storage, and the database is searched and information is acquired according to application requirements, so that the searching efficiency and the information acquisition capacity are limited on one hand, the information in the database cannot be visually checked on the other hand, and the utilization value of the data is greatly limited.
In the prior art, the interference of external factors on electromagnetic waves cannot be effectively removed, and the imaging effect is reduced; in the prior art, the image segmentation effect is poor, and the image processing and analyzing effects are not good; in the prior art, the classification quality cannot be guaranteed during data classification processing, and the classification speed of remote sensing data is low. In the prior art, the effect of extracting the landform characteristics of the mountain remote sensing image is poor, and the method cannot be well applied to a ground surface with large fluctuation.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a mountain landform remote sensing extraction method.
The invention is realized in such a way that a mountain landform remote sensing extraction method comprises the following steps:
firstly, detecting electromagnetic waves emitted by a mountain land by using a remote sensor;
secondly, generating a mountain remote sensing image by using the detected electromagnetic waves through an imaging device;
correcting the generated mountain remote sensing image by using correction software; carrying out segmentation operation on the mountain remote sensing image by using image segmentation software;
extracting the landform characteristics of the mountain remote sensing image by using image processing software; randomly deploying M sensor nodes in a mountain detection part, and performing Delaunay triangle subdivision on the central point of each node; making a circumscribed circle of each Delaunay triangle, comparing the radius of the node with the radius of the circumscribed circle, if R is larger than R, determining that a hidden part exists, storing the Delaunay triangle and the circumscribed circle, otherwise, removing the circumscribed circle, wherein the radius of the node of the sensor is R, and the radius of each circumscribed circle is R; calculating the common side length d of the rest two adjacent triangles, and if d is greater than 2r or the common side length is not intersected with the central connecting line of the circumscribed circles of the two triangles, clustering and grouping the triangles to obtain boundary nodes, wherein each clustering group has a hidden part; for the center point of the sensor node in each cluster group, representing the boundary of the hidden part by using a method of a minimum polygon capable of containing the hidden part; judging a false boundary node for the boundary node, after removing the false boundary node, expressing the improved boundary of the hidden part by using a minimum polygon method capable of containing the hidden part again; the actual coverage area of the sensor nodes randomly deployed at the mountain detection part is reduced, the two-dimensional coverage area of the sensor nodes subjected to terrain correction is an ellipse, the actual detection radius is calculated by utilizing the slope and the slope angle, the corrected hidden part boundary is calculated by using a detection algorithm, and finally the landform characteristics of the mountain remote sensing image are obtained;
step five, carrying out classification processing operation on the remote sensing data by using data processing software;
step six, storing the mountain remote sensing image data by using a memory through a remote sensing image storage module; and the mountain remote sensing image is displayed by the display module through the display.
Further, the image segmentation method comprises the following steps:
(1) Reading a high-resolution remote sensing image to be segmented;
(2) Calculating the Gaussian two-type fuzzy membership degree corresponding to each gray level by using a Gaussian two-type fuzzy membership function model of each feature class in the high-resolution remote sensing image to be segmented;
(3) Calculating the membership degree of each gray level in each segmentation decision model by using the segmentation decision model of each object class in the high-resolution remote sensing image to be segmented;
(4) The ground object class corresponding to the maximum membership value of the gray level of each pixel in each segmentation decision model in the high-resolution remote sensing image is a segmentation result;
(5) Changing a Gaussian two-type fuzzy membership function model according to the set step length, repeating the steps (2) to (4), comparing all segmentation results, and taking the segmentation result with the highest segmentation precision as a final high-resolution remote sensing image segmentation result;
further, the step (2) includes:
constructing a Gaussian main membership function model and calculating a main membership degree: carrying out supervised sampling on each ground feature class in the high-resolution remote sensing image to be segmented to extract a training sample, calculating the frequency of each gray level in the training sample appearing in the corresponding ground feature class, establishing a Gaussian main membership function model for different ground feature classes and calculating the Gaussian main membership degree;
determining an uncertain region of a Gaussian two-type fuzzy membership function model: the standard difference in the Gaussian main membership function model is fuzzified into a standard difference interval, a region formed by the Gaussian main membership function model corresponding to the standard difference interval is an uncertain region of the Gaussian two-type fuzzy membership function model, and at the moment, the Gaussian main membership degree corresponding to each gray level is an interval;
constructing a Gaussian subordination function model: determining the mean value and the variance of a Gaussian subordination function model of each gray level in the gray level range, establishing a Gaussian subordination function model and calculating the Gaussian subordination degree;
calculating the Gaussian second type fuzzy membership degree by utilizing a Gaussian second type fuzzy membership function model consisting of a Gaussian main membership function model, a Gaussian secondary membership function model and an uncertain region: calculating the product of the Gaussian primary membership set element and the corresponding Gaussian secondary membership set element of each gray level in the gray level range, namely the Gaussian secondary fuzzy membership of the gray level, wherein the Gaussian secondary fuzzy membership corresponding to each gray level is a set;
further, the remote sensing data classification method comprises the following steps:
1) Dividing the mass remote sensing data into at least one data set according to the spatial information and the time information of each remote sensing data in the mass remote sensing data, wherein each data set comprises at least one remote sensing data;
2) Extracting data features in each data set, the data features comprising: the system comprises attribute features and image features, wherein the attribute features refer to the source, type and resolution of data, and the image features refer to histogram features, edge features and texture features;
3) According to the data characteristics, carrying out hierarchical clustering on the remote sensing data in each data set, thereby classifying the remote sensing data with the same data characteristics in each data set into the same data category;
4) Adding a semantic tag to each data category;
further, the step 1) includes:
coding each remote sensing data according to a geographic space position under a standard ellipsoid coordinate system to obtain a geocode of each remote sensing data, wherein the geocode comprises: the hierarchy, longitude and latitude of the data; dividing a global space range according to longitude and latitude and altitude grids and numbering, wherein the geocode consists of 20 bits, the first two bits represent altitude numbers, the middle 9 bits represent longitude numbers, and the last 9 bits represent latitude numbers;
merging the remote sensing data with the same geocode into the same data set to obtain at least one data set;
in each data set, establishing a sequence relation according to the time information of each remote sensing data;
establishing a time-space index for the data set with the established sequence relation;
further, in the step 3), hierarchical clustering is carried out on the remote sensing data by adopting a restaurant-in-hierarchy model;
when a restaurant model in a hierarchy is adopted to carry out hierarchical clustering on any remote sensing data, classifying the remote sensing data into an existing data category, or newly establishing a data category, and classifying the remote sensing data into the newly established data category;
and carrying out hierarchical clustering on the newly-added remote sensing data by adopting a restaurant-in-hierarchy model, classifying the newly-added remote sensing data into an existing data category, or newly establishing a data category, and classifying the newly-added remote sensing data into the newly-established data category.
Further, the method for randomly deploying the sensor nodes in the mountain detection part is characterized in that the mountain detection part is represented by a single-value function z = h (x, y), the sensing radius r of each sensor is the same, and the sensing area forms a sphere which is centered on the sensor position in a three-dimensional space and has the radius of r.
The invention also aims to provide a terminal, and the terminal is provided with a processor for realizing the mountain landform remote sensing extraction method.
Another object of the present invention is to provide a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to execute the mountain landform remote sensing extraction method.
Another object of the present invention is to provide a mountain land feature remote sensing extraction system for implementing the mountain land feature remote sensing extraction, the mountain land feature remote sensing extraction system comprising:
the electromagnetic wave detection module is connected with the main control module and used for detecting the electromagnetic waves emitted by the mountainous region through a remote sensor;
the main control module is connected with the electromagnetic wave detection module, the remote sensing image generation module, the correction module, the image segmentation module, the feature extraction module, the remote sensing data classification module, the remote sensing image storage module and the display module and is used for controlling each module to normally work through the single chip microcomputer;
the remote sensing image generation module is connected with the main control module and used for generating a mountain remote sensing image from the detected electromagnetic waves through the imaging equipment;
the correction module is connected with the main control module and used for correcting the generated mountain remote sensing image through correction software;
the image segmentation module is connected with the main control module and is used for carrying out segmentation operation on the mountain remote sensing image through image segmentation software;
the characteristic extraction module is connected with the main control module and used for extracting the landform characteristics of the mountain remote sensing image through image processing software;
the remote sensing data classification module is connected with the main control module and is used for performing classification processing operation on the remote sensing data through data processing software;
the remote sensing image storage module is connected with the main control module and used for storing mountain remote sensing image data through the memory;
and the display module is connected with the main control module and used for displaying the mountain remote sensing image through the display.
The invention has the advantages and positive effects that:
according to the method, the image segmentation module is used for constructing a Gaussian two-type fuzzy membership function model for the image and constructing a segmentation decision model by weighted average of all membership degrees, so that the segmentation problems caused by uncertainty of gray level membership and uncertainty of segmentation decision are effectively solved, more accurate fitting of distribution characteristics of a complex histogram of high-resolution remote sensing data is realized, noise is well overcome, and the segmentation precision of the mountain land remote sensing appearance image is improved; meanwhile, the classification system learning and the spatio-temporal data self-organization are utilized by the sensory data classification module, the hierarchical classification system is automatically established according to the data, the clustering result which is refined from coarse to fine is provided, the flexibility and the convenience are realized, in the subsequent use process, the hierarchical classification system can be dynamically and continuously adjusted and perfected according to new data, and the dynamic organization and the classification management of the mountain remote sensing data are realized.
According to the imaging equipment, interference of external factors on electromagnetic waves is effectively removed through a wavelet domain denoising model of PURE-LET in the process of generating the mountain remote sensing image, and the imaging effect is improved; by using the image segmentation software and adopting the FCM image segmentation algorithm through the computer, the segmentation effect of the mountain remote sensing image is effectively improved, the segmentation speed is accelerated, the segmentation speed is improved, and the image processing analysis effect is favorably improved; and the remote sensing data is classified by using data processing software and adopting a naive Bayes algorithm, so that the classification speed of the remote sensing data is improved under the condition of ensuring the classification quality.
The method comprises the steps of randomly deploying M sensor nodes in a mountain detection part, and performing Delaunay triangle subdivision on the center point of each node; making a circumscribed circle of each Delaunay triangle, comparing the radius of the node with the radius of the circumscribed circle, if R is greater than R, determining that a hidden part exists, storing the Delaunay triangle and the circumscribed circle, and if not, removing the circumscribed circle, wherein the radius of the node of the sensor is R, and the radius of each circumscribed circle is R; calculating the public side length d of the rest two adjacent triangles, and if d is greater than 2r or the public side is not intersected with the central connecting line of the circumscribed circles of the two triangles, clustering and grouping the triangles to obtain boundary nodes, wherein each clustering and grouping can have a hidden part; for the center point of the sensor node in each cluster group, representing the boundary of the hidden part by using a method of a minimum polygon capable of containing the hidden part; judging a false boundary node for the boundary node, after removing the false boundary node, expressing the improved boundary of the hidden part by using a minimum polygon method capable of containing the hidden part again; the actual coverage area of the sensor nodes randomly deployed at the mountain detection part is reduced, the two-dimensional coverage area of the sensor nodes subjected to terrain correction is an ellipse, the actual detection radius is calculated by utilizing the slope and the slope angle, the corrected hidden part boundary is calculated by using a detection algorithm, and finally the landform characteristics of the mountain remote sensing image are obtained; can be well applied to the ground surface with large undulation.
Drawings
Fig. 1 is a flow chart of a mountain landform remote sensing extraction method provided by the embodiment of the invention.
Fig. 2 is a structural diagram of a mountain landform remote sensing extraction system provided in an embodiment of the present invention.
In the figure: 1. an electromagnetic wave detection module; 2. a main control module; 3. a remote sensing image generation module; 4. a correction module; 5. an image segmentation module; 6. a feature extraction module; 7. a remote sensing data classification module; 8. a remote sensing image storage module; 9. and a display module.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are exemplified and included in the detailed description with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in FIG. 1, the remote sensing extraction method of mountain landform provided by the invention comprises the following steps:
and S101, detecting and collecting electromagnetic waves emitted by the mountainous region by using a remote sensor.
And S102, generating a mountain remote sensing image from the detected electromagnetic waves by using an imaging device with a wavelet domain denoising model of PURE-LET.
And S103, correcting the generated mountain remote sensing image by using correction software through a computer. And (3) segmenting the mountain remote sensing image by using image segmentation software and adopting an FCM image segmentation algorithm through a computer.
And S104, extracting the landform characteristics of the mountain remote sensing image by using image processing software through a computer. And classifying the remote sensing data by using data processing software and adopting a naive Bayesian algorithm through a computer.
And S105, storing the mountain remote sensing image data by using a memory. And displaying the mountain remote sensing image through a display.
In step S102, the imaging device provided in the embodiment of the present invention effectively removes interference of external factors on electromagnetic waves through a wavelet domain denoising model of PURE-LET in the process of generating a mountain remote sensing image, and improves an imaging effect, where a specific algorithm is as follows:
estimating wavelet coefficients at each scale
Are written as a linear combination of a set of basic threshold functions:
and determining a coefficient vector a = [ a ] by minimization of PURE 1 ，…，a M ] T 。
Let θ (d, s) = θ j (d j ，s j ) Is a noiseless wavelet coefficient delta = delta j An estimate of (2).
Function theta + (d, s) and θ － (d, s) comprises:
wherein,
is->
Standard base of (3), except for e k (k) And the other elements except 1 are all 0. Then the random variable PURE j For unbiased estimation of MSE under subband j, i.e. E { PURE j }＝E{MSE j }。
The linear combination parameters of wavelet estimation in equation (2) are calculated by minimization of the PURE. Substituting formula (2) into formula (3), and omitting the independent variables (d, s) having
In step S103, the computer provided by the embodiment of the present invention uses the image segmentation software to adopt the FCM image segmentation algorithm, so as to effectively improve the mountain remote sensing image segmentation effect, accelerate the segmentation speed, improve the segmentation speed, and advantageously improve the image processing analysis effect. The method comprises the following specific steps:
(1) Determination of initialization: according to the requirements of image segmentation, the image needs to be initialized, the needed parameters need to be initialized, and the clustering center of the histogram needs to be determined.
(2) Determining the adaptivity of the factor, and the fitness according to the constructed adaptive function:
f＝a/(b+J)。
wherein a and b are adjustable parameters, which can be respectively 10 and 1.5 according to experiments, and J is an objective function.
(3) Mutation operation: the variation amount between before and after the individuals is 0.5r (T/T), data r is a random number generated in a predetermined interval, and T is a maximum algebraic number calculated.
(4) And (3) iterative calculation: and obtaining a new fuzzy membership matrix through the new cutting data, generating new cutting parameters, returning to the step two for iterative calculation until the completion condition is terminated, and completing the segmentation of the image.
In step S104, the data processing software provided in the embodiment of the present invention performs classification processing on the remote sensing data by using a naive bayes algorithm, and improves the classification speed of the remote sensing data under the condition of ensuring the classification quality, where the specific algorithm is as follows:
let D be the set of class labels with which the training object is associated. Using one n-dimensional attribute vector X = { X) per object 1 ，x 2 …x n Denotes, describing n attributes A 1 ，A 2 …A n The value of (c). Assume that the original set is co-partitioned into m classes C based on n-dimensional attributes 1 ，C 2 …C m The posterior probability of each class to X is calculated and object X is attributed to the class with the highest posterior probability. Posterior probability P (C) i | X) is calculated as:
due to P (C) i I X), making an assumption that class conditions are independent, giving class labels of vectors, and assuming that attribute values are conditionally independent from each other. P (X) i The formula for | C) is:
wherein, P (x) 1 |C i )P(x 2 |C i )…P(x n |C n ) Can be easily calculated from the training subjects, x k Indicates that X is in the attribute A k The value of (c) above. For each class C i Calculate P (X | C) i )P(C i ). When P (X | C) i )P(C i )>P(X|C j )P(C j ) J is not less than 1 and not more than m, and when j is not equal to i, X belongs to the class C i 。
In step S104, M sensor nodes are randomly deployed in the mountain detection part, and Delaunay triangle subdivision is performed on the center point of each node.
Making a circumscribed circle of each Delaunay triangle, comparing the radius of the node with the radius of the circumscribed circle, if R is greater than R, determining that a hidden part exists, storing the Delaunay triangle and the circumscribed circle, and if not, removing the circumscribed circle, wherein the radius of the node of the sensor is R, and the radius of each circumscribed circle is R; calculating the public side length d of the rest two adjacent triangles, and if d is greater than 2r or the public side is not intersected with the central connecting line of the circumscribed circles of the two triangles, clustering and grouping the triangles to obtain boundary nodes, wherein each clustering and grouping can have a hidden part; for the center point of the sensor node in each cluster group, representing the boundary of the hidden part by using a method of a minimum polygon capable of containing the hidden part; judging a false boundary node for the boundary node, after removing the false boundary node, expressing the improved boundary of the hidden part by using a minimum polygon method capable of containing the hidden part again; the actual coverage area of the sensor nodes randomly deployed at the mountain land detection part can be reduced, the two-dimensional coverage area of the sensor nodes subjected to terrain correction is an ellipse, the actual detection radius is calculated by utilizing the slope and the slope angle, the corrected hidden part boundary is calculated by using a detection algorithm, and finally the landform characteristics of the mountain land remote sensing image are obtained.
The method comprises the steps that sensor nodes are randomly deployed in mountain detection parts, the mountain detection parts are expressed as a single-value function z = h (x, y), the sensing radius r of each sensor is the same, and a sensing area forms a sphere which is centered on the position of the sensor in a three-dimensional space and has the radius of r.
The actual detection radius is calculated by the following method for the gradient of the point P (x, y) on the curved surface z = h (x, y):
wherein
And &>
Respectively, partial derivatives in x and y directions, i and j are unit vectors, and the mode of the directional gradient is a gradient;
the slope G of point P in the direction β is:
G＝Scosβ
beta is a slope direction, and due to the fluctuation defect of the three-dimensional terrain, the relation between the actual detection radius r' of the sensor node along the beta direction and the ideal detection radius r is expressed as follows:
r'＝rcosγ
the actual detection radius r' is related to the slope S and the slope angle β by:
r'＝rcos(arctan(Scosβ))。
the correction method is that along the direction of a slope, the difference value between two intersecting contour lines of the nodes is a height difference delta h, the distance between the two intersecting contour lines is delta d, and the slope S is expressed as:
and calculating the elliptical projection of each sensor node on a two-dimensional plane under the three-dimensional terrain.
As shown in fig. 2, the mountain landform remote sensing extraction system provided by the embodiment of the present invention includes:
the remote sensing image acquisition system comprises an electromagnetic wave detection module 1, a main control module 2, a remote sensing image generation module 3, a correction module 4, an image segmentation module 5, a feature extraction module 6, a remote sensing data classification module 7, a remote sensing image storage module 8 and a display module 9.
And the electromagnetic wave detection module 1 is connected with the main control module 2 and is used for detecting the electromagnetic waves emitted by the mountainous region through a remote sensor.
The main control module 2 is connected with the electromagnetic wave detection module 1, the remote sensing image generation module 3, the correction module 4, the image segmentation module 5, the feature extraction module 6, the remote sensing data classification module 7, the remote sensing image storage module 8 and the display module 9, and is used for controlling each module to normally work through the single chip microcomputer.
And the remote sensing image generation module 3 is connected with the main control module 2 and is used for generating the mountain remote sensing image by the detected electromagnetic waves through the imaging equipment.
And the correction module 4 is connected with the main control module 2 and is used for correcting the generated mountain remote sensing image through correction software.
And the image segmentation module 5 is connected with the main control module 2 and is used for carrying out segmentation operation on the mountain remote sensing image through image segmentation software.
And the feature extraction module 6 is connected with the main control module 2 and is used for extracting the landform features of the mountain remote sensing image through image processing software.
And the remote sensing data classification module 7 is connected with the main control module 2 and is used for performing classification processing operation on the remote sensing data through data processing software.
And the remote sensing image storage module 8 is connected with the main control module 2 and is used for storing the mountain remote sensing image data through a memory.
And the display module 9 is connected with the main control module 2 and used for displaying the mountain remote sensing image through a display.
The image segmentation module 5 provided by the invention comprises the following segmentation methods:
(1) And reading the high-resolution remote sensing image to be segmented.
(2) And calculating the Gaussian second type fuzzy membership degree corresponding to each gray level by using the Gaussian second type fuzzy membership function model of each feature class in the high-resolution remote sensing image to be segmented.
(3) And calculating the membership degree of each gray level in each segmentation decision model by using the segmentation decision model of each object class in the high-resolution remote sensing image to be segmented.
(4) And the ground object class corresponding to the maximum membership value of the gray level of each pixel in each segmentation decision model in the high-resolution remote sensing image is the segmentation result.
(5) And (3) changing the Gaussian two-type fuzzy membership function model according to the set step length, repeating the steps (2) to (4), comparing all segmentation results, and taking the segmentation result with the highest segmentation precision as a final high-resolution remote sensing image segmentation result.
The step (2) provided by the invention comprises the following steps:
constructing a Gaussian main membership function model and calculating a main membership degree: carrying out supervised sampling on each ground feature class in the high-resolution remote sensing image to be segmented to extract a training sample, calculating the frequency of each gray level in the training sample appearing in the corresponding ground feature class, establishing a Gaussian main membership function model for different ground feature classes and calculating the Gaussian main membership degree.
Determining an uncertain region of a Gaussian two-type fuzzy membership function model: and the standard deviation in the Gaussian main membership function model is fuzzified into a standard deviation interval, an area formed by the Gaussian main membership function model corresponding to the standard deviation interval is an uncertain area of the Gaussian two-type fuzzy membership function model, and at the moment, the Gaussian main membership degree corresponding to each gray level is an interval.
Constructing a Gaussian subordination function model: and determining the mean value and the variance of the Gaussian subordination function model of each gray level in the gray level range, establishing the Gaussian subordination function model and calculating the Gaussian subordination degree.
Calculating the Gaussian second type fuzzy membership degree by utilizing a Gaussian second type fuzzy membership function model consisting of a Gaussian main membership function model, a Gaussian secondary membership function model and an uncertain region: and calculating the product of the Gaussian primary membership set element and the corresponding Gaussian secondary membership set element of each gray level in the gray level range, namely the Gaussian secondary fuzzy membership of the gray level, wherein the Gaussian secondary fuzzy membership corresponding to each gray level is a set.
The remote sensing data classification module 7 provided by the invention comprises the following classification methods:
1) According to the spatial information and the time information of each remote sensing data in the mass remote sensing data, the mass remote sensing data is divided into at least one data set, wherein each data set comprises at least one remote sensing data.
2) Extracting data features in each data set, the data features including: the image feature comprises an attribute feature and an image feature, wherein the attribute feature refers to the source, type and resolution of data, and the image feature refers to a histogram feature, an edge feature and a texture feature.
3) And carrying out hierarchical clustering on the remote sensing data in each data set according to the data characteristics, thereby classifying the remote sensing data with the same data characteristics in each data set into the same data category.
4) A semantic tag is added to each data category.
The invention provides a step 1) which comprises the following steps:
coding each remote sensing data according to a geographic space position under a standard ellipsoid coordinate system to obtain a geocode of each remote sensing data, wherein the geocode comprises: the level, longitude and latitude of the data. The global space range is divided and numbered according to longitude and latitude and altitude grids, the geocode is composed of 20 bits, the first two bits represent altitude numbers, the middle 9 bits represent longitude numbers, and the last 9 bits represent latitude numbers.
Merging the remote sensing data with the same geocode into the same data set to obtain at least one data set.
And in each data set, establishing a sequence relation according to the time information of each remote sensing data.
And establishing a space-time index for the data set with the established sequence relation.
In the step 3), the remote sensing data is hierarchically clustered by adopting a restaurant model in the hierarchy.
When a restaurant model in a hierarchy is adopted to carry out hierarchical clustering on any remote sensing data, the remote sensing data is classified into the existing data category, or a data category is newly established, and the remote sensing data is classified into the newly established data category.
And carrying out hierarchical clustering on the newly-added remote sensing data by adopting a restaurant-in-hierarchy model, classifying the newly-added remote sensing data into an existing data category, or newly establishing a data category, and classifying the newly-added remote sensing data into the newly-established data category.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (10)
1. A mountain landform remote sensing extraction method is characterized by comprising the following steps:
firstly, detecting electromagnetic waves emitted from a mountain land by using a remote sensor;
secondly, generating a mountain remote sensing image by using the detected electromagnetic waves through an imaging device;
correcting the generated mountain remote sensing image by using correction software; segmenting the mountain remote sensing image by using image segmentation software;
extracting the landform characteristics of the mountain remote sensing image by using image processing software; randomly deploying M sensor nodes in a mountain detection part, and performing Delaunay triangle subdivision on the central point of each node; making a circumscribed circle of each Delaunay triangle, comparing the radius of the node with the radius of the circumscribed circle, if R is greater than R, determining that a hidden part exists, storing the Delaunay triangle and the circumscribed circle, and if not, removing the circumscribed circle, wherein the radius of the node of the sensor is R, and the radius of each circumscribed circle is R; calculating the public side length d of the rest two adjacent triangles, and if d is greater than 2r or the public side is not intersected with the central connecting line of the circumscribed circles of the two triangles, clustering and grouping the triangles to obtain boundary nodes, wherein each clustering and grouping can have a hidden part; for the center point of the sensor node in each cluster group, representing the boundary of the hidden part by using a method of a minimum polygon capable of containing the hidden part; judging a false boundary node for the boundary node, after removing the false boundary node, expressing the improved boundary of the hidden part by using a minimum polygon method capable of containing the hidden part again; the actual coverage area of the sensor nodes randomly deployed at the mountain land detection part is reduced, the two-dimensional coverage area of the sensor nodes subjected to terrain correction is an ellipse, the actual detection radius is calculated by utilizing the slope and the slope angle, the corrected hidden part boundary is calculated by utilizing a detection algorithm, and finally the landform characteristics of the mountain land remote sensing image are obtained;
step five, carrying out classification processing operation on the remote sensing data by using data processing software;
step six, storing the mountain remote sensing image data by using a memory through a remote sensing image storage module; and the mountain remote sensing image is displayed by the display module through the display.
2. The remote sensing extraction method of mountain land features as claimed in claim 1, wherein the image segmentation method comprises:
(1) Reading a high-resolution remote sensing image to be segmented;
(2) Calculating the Gaussian two-type fuzzy membership degree corresponding to each gray level by using a Gaussian two-type fuzzy membership function model of each feature class in the high-resolution remote sensing image to be segmented;
(3) Calculating the membership degree of each gray level in each segmentation decision model by using the segmentation decision model of each object class in the high-resolution remote sensing image to be segmented;
(4) The ground object category corresponding to the maximum membership value of the gray level of each pixel in each segmentation decision model in the high-resolution remote sensing image is a segmentation result;
(5) And (3) changing the Gaussian two-type fuzzy membership function model according to the set step length, repeating the steps (2) to (4), comparing all segmentation results, and taking the segmentation result with the highest segmentation precision as a final high-resolution remote sensing image segmentation result.
3. The remote sensing extraction method of mountain land features as claimed in claim 2, wherein said step (2) comprises:
constructing a Gaussian main membership function model and calculating a main membership degree: carrying out supervised sampling on each ground feature class in the high-resolution remote sensing image to be segmented to extract a training sample, calculating the frequency of each gray level in the training sample appearing in the corresponding ground feature class, establishing a Gaussian main membership function model for different ground feature classes and calculating the Gaussian main membership degree;
determining an uncertain region of the Gaussian two-type fuzzy membership function model: the standard difference in the Gaussian main membership function model is fuzzified into a standard difference interval, a region formed by the Gaussian main membership function model corresponding to the standard difference interval is an uncertain region of the Gaussian two-type fuzzy membership function model, and at the moment, the Gaussian main membership degree corresponding to each gray level is an interval;
constructing a Gaussian subordination function model: determining the mean value and the variance of a Gaussian subordination function model of each gray level in the gray level range, establishing a Gaussian subordination function model and calculating the Gaussian subordination degree;
calculating the Gaussian second type fuzzy membership degree by utilizing a Gaussian second type fuzzy membership function model consisting of a Gaussian main membership function model, a Gaussian secondary membership function model and an uncertain region: and calculating the product of the Gaussian primary membership set element and the corresponding Gaussian secondary membership set element of each gray level in the gray level range, namely the Gaussian secondary fuzzy membership of the gray level, wherein the Gaussian secondary fuzzy membership corresponding to each gray level is a set.
4. The mountain landform remote sensing extraction method as claimed in claim 1, wherein the remote sensing data classification method comprises:
1) Dividing the mass remote sensing data into at least one data set according to the spatial information and the time information of each remote sensing data in the mass remote sensing data, wherein each data set comprises at least one remote sensing data;
2) Extracting data features in each data set, the data features comprising: the system comprises attribute features and image features, wherein the attribute features refer to the source, type and resolution of data, and the image features refer to histogram features, edge features and texture features;
3) According to the data characteristics, carrying out hierarchical clustering on the remote sensing data in each data set, so as to classify the remote sensing data with the same data characteristics in each data set into the same data category;
4) A semantic tag is added for each data category.
5. The remote sensing extraction method of mountain landform as claimed in claim 4, wherein the step 1) comprises:
coding each remote sensing data according to a geographic space position under a standard ellipsoid coordinate system to obtain a geocode of each remote sensing data, wherein the geocode comprises: the level, longitude and latitude of the data; dividing a global space range according to longitude, latitude and altitude grids and numbering, wherein the geocode consists of 20 bits, the first two bits represent altitude numbers, the middle 9 bits represent longitude numbers, and the last 9 bits represent latitude numbers;
merging the remote sensing data with the same geocode into the same data set to obtain at least one data set;
in each data set, establishing a sequence relation according to the time information of each remote sensing data;
and establishing a space-time index for the data set with the established sequence relation.
6. The remote sensing extraction method of mountain landform as claimed in claim 4, wherein in step 3), a hierarchical restaurant model is used to perform hierarchical clustering on the remote sensing data;
when a restaurant model in a hierarchy is adopted to carry out hierarchical clustering on any remote sensing data, classifying the remote sensing data into an existing data category, or newly establishing a data category, and classifying the remote sensing data into the newly established data category;
and carrying out hierarchical clustering on the newly-added remote sensing data by adopting a restaurant-in-hierarchy model, classifying the newly-added remote sensing data into an existing data category, or newly establishing a data category, and classifying the newly-added remote sensing data into the newly-established data category.
7. The remote sensing extraction method of mountain land features as claimed in claim 1, wherein the sensor nodes are randomly deployed in mountain land detection positions, the mountain land detection positions are expressed as a single-valued function z = h (x, y), the sensing radius r of each sensor is the same, and the sensing area forms a sphere centered on the sensor position and having a radius r in a three-dimensional space.
8. A terminal, characterized in that the terminal is equipped with a processor for implementing the mountain landform remote sensing extraction method of any one of claims 1 to 7.
9. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the remote mountain relief feature extraction method of any one of claims 1-7.
10. A mountain land feature remote sensing extraction system for implementing the mountain land feature remote sensing extraction method of claim 1, wherein the mountain land feature remote sensing extraction system comprises:
the electromagnetic wave detection module is connected with the main control module and used for detecting the electromagnetic waves emitted by the mountainous region through a remote sensor;
the main control module is connected with the electromagnetic wave detection module, the remote sensing image generation module, the correction module, the image segmentation module, the feature extraction module, the remote sensing data classification module, the remote sensing image storage module and the display module and is used for controlling each module to normally work through the single chip microcomputer;
the remote sensing image generation module is connected with the main control module and used for generating a mountain remote sensing image from the detected electromagnetic waves through the imaging equipment;
the correction module is connected with the main control module and used for correcting the generated mountain remote sensing image through correction software;
the image segmentation module is connected with the main control module and is used for carrying out segmentation operation on the mountain remote sensing image through image segmentation software;
the characteristic extraction module is connected with the main control module and used for extracting the landform characteristics of the mountain remote sensing image through image processing software;
the remote sensing data classification module is connected with the main control module and is used for performing classification processing operation on the remote sensing data through data processing software;
the remote sensing image storage module is connected with the main control module and used for storing mountain remote sensing image data through the memory;
and the display module is connected with the main control module and used for displaying the mountain remote sensing image through the display.
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